Accelerated K-means Clustering Algorithm

Jain, Preeti; Buksh, Bala

doi:10.5815/ijitcs.2016.10.05

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…The only information clustering uses is similarity between data points .Many researchers [10] have been dealt with clustering problem in different disciplines. The clustering method can be divided into hierarchical and nonhierarchical [13].…”

Section: Methodsmentioning

confidence: 99%

Design and Implementation of an Intelligent Mobile Game

Ekinci¹,

Gülağız²,

Omurca³

2017

IJMECS

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Abstract-While the mobile game industry is growing with each passing day with the popularization of 3G smart devices, the creation of successful games, which may interest users, become quite important in terms of the survival of the designed game. Clustering, which has many application fields, is a successful method and its implementation in the field of mobile games is inevitable. In this study, classical ball blasting game was carried out based on clustering. In the game, clustering the color codes with K-means, Iterative K-means, Iterative Multi K-means and K-medoids methods and blasting the balls of colors located in the same cluster by bringing them together were proposed. As a result of the experiments, the suitability of clustering methods for mobile based ball blasting game was shown. At the same time, the clustering methods were compared to produce the more successful clusters and because of obtaining more accurate results and stability, the use of K-medoids method has been chosen for this game.

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Section: Methodsmentioning

confidence: 99%

Design and Implementation of an Intelligent Mobile Game

Ekinci¹,

Gülağız²,

Omurca³

2017

IJMECS

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“…Authors propose [27] method to optimize a number of clusters k, with minimum time complexity. This reduces the effort required for each iteration by decreasing reclustering of data objects.…”

Section: Related Workmentioning

confidence: 99%

Efficient Clustering Algorithm with Enhanced Cohesive Quality Clusters

Khandare¹,

Alvi²

2018

IJISA

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Analyzing data is a challenging task nowadays because the size of data affects results of the analysis. This is because every application can generate data of massive amount. Clustering techniques are key techniques to analyze the massive amount of data. It is a simple way to group similar type data in clusters. The key examples of clustering algorithms are k-means, kmedoids, c-means, hierarchical and DBSCAN. The kmeans and DBSCAN are the scalable algorithms but again it needs to be improved because massive data hampers the performance with respect to cluster quality and efficiency of these algorithms. For these algorithms, user intervention is needed to provide appropriate parameters as an input. For these reasons, this paper presents modified and efficient clustering algorithm. This enhances cluster's quality and makes clusters more cohesive using domain knowledge, spectral analysis, and split-merge-refine techniques. Also, this algorithm takes care to minimizing empty clusters. So far no algorithm has integrated these all requirements that proposed algorithm does just as a single algorithm. It also automatically predicts the value of k and initial centroids to have minimum user intervention with the algorithm. The performance of this algorithm is compared with standard clustering algorithms on various small to large data sets. The comparison is with respect to a number of records and dimensions of data sets using clustering accuracy, running time, and various clusters validly measures. From the obtained results, it is proved that performance of proposed algorithm is increased with respect to efficiency and quality than the existing algorithms.

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“…However, it involves many iterations for distance computation between points and cluster centers and due to increased cost per iteration, the scalability of the algorithm gets hampered. Improving the algorithm in this aspect is a focus of many optimization types of research [17].…”

Section: B Data Miningmentioning

confidence: 99%

Decision Support System to Determine Promotional Methods and Targets with K-Means Clustering

Utami¹

2018

IJIEEB

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Promotion becomes one of the important aspects of institutions of college. The number of competitors demanding the marketing must be fast and accurate in formulating strategies and decision making. Data warehouse and data mining become one of the means to build a decision support system that can provide knowledge and wisdom quickly to be taken into consideration in promotion strategy planning. Development of this system then does the process of testing with the number of data 6171 rows of student enrollment taken directly from a transactional database. The data is done ETL process and clustering with the kmeans clustering algorithm, then the data in each cluster is done grouping and summarization to get weighting. After that just done ranking to produce wisdom, one of them determine the list of schools that will be the target roadshow. The analysis also produces several patterns of student enrollment, namely the registrant pattern from the wave of registration and favorite or non-favorite school categories. In addition, the results of system design in this study can be developed easily if requires added external data. Such as data of SMK/SMK school graduates in the area or data of students enrolling in other universities. This is one of the superiority of semantic-based data warehouses.

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Accelerated K-means Clustering Algorithm

Cited by 5 publications

References 13 publications

Design and Implementation of an Intelligent Mobile Game

Design and Implementation of an Intelligent Mobile Game

Efficient Clustering Algorithm with Enhanced Cohesive Quality Clusters

Decision Support System to Determine Promotional Methods and Targets with K-Means Clustering

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